我们团队使用Go来开发游戏服务端，游戏服务端的战斗逻辑避免不了各种计算，C的编程经验告诉我们整数运算效率比浮点运算高，但是在Go语言中，这两种数据类型的运算效率到底差多少呢？是否有必要把浮点数转换成整数的万分数或者千分数进行运算呢？下面通过实验来对比。
实验代码（github）：
package labs05
import "testing"
func Benchmark_IntAdd(b *testing.B) {
var a = 0
for i := 0; i < b.N; i++ {
a += 1
}
}
func Benchmark_Int8Add(b *testing.B) {
var a int8 = 0
for i := 0; i < b.N; i++ {
a += 1
}
}
func Benchmark_Int16Add(b *testing.B) {
var a int8 = 0
for i := 0; i < b.N; i++ {
a += 1
}
}
func Benchmark_Int32Add(b *testing.B) {
var a int32 = 0
for i := 0; i < b.N; i++ {
a += 1
}
}
func Benchmark_Int64Add(b *testing.B) {
var a int64 = 0
for i := 0; i < b.N; i++ {
a += 1
}
}
func Benchmark_Float32Add(b *testing.B) {
var a float32 = 0.1
for i := 0; i < b.N; i++ {
a += 1.0
}
}
func Benchmark_Float64Add(b *testing.B) {
var a float64 = 0.1
for i := 0; i < b.N; i++ {
a += 1.0
}
}
func Benchmark_IntSub(b *testing.B) {
var a = 0x7FFFFFFFFF
for i := 0; i < b.N; i++ {
a -= 1
}
}
func Benchmark_Int8Sub(b *testing.B) {
var a int8 = 0x7F
for i := 0; i < b.N; i++ {
a -= 1
}
}
func Benchmark_Int16Sub(b *testing.B) {
var a int16 = 0x7FFF
for i := 0; i < b.N; i++ {
a -= 1
}
}
func Benchmark_Int32Sub(b *testing.B) {
var a int32 = 0x7FFFFFFF
for i := 0; i < b.N; i++ {
a -= 1
}
}
func Benchmark_Int64Sub(b *testing.B) {
var a int64 = 0x7FFFFFFFFF
for i := 0; i < b.N; i++ {
a -= 1
}
}
func Benchmark_Float32Sub(b *testing.B) {
var a = float32(0x7FFFFFFF)
for i := 0; i < b.N; i++ {
a -= 1.0
}
}
func Benchmark_Float64Sub(b *testing.B) {
var a = float64(0xFFFFFFFFFF)
for i := 0; i < b.N; i++ {
a -= 1.0
}
}
func Benchmark_IntMul(b *testing.B) {
var a = 1
for i := 0; i < b.N; i++ {
a *= 3
}
}
func Benchmark_Int8Mul(b *testing.B) {
var a int8 = 1
for i := 0; i < b.N; i++ {
a *= 3
}
}
func Benchmark_Int16Mul(b *testing.B) {
var a int16 = 1
for i := 0; i < b.N; i++ {
a *= 3
}
}
func Benchmark_Int32Mul(b *testing.B) {
var a int32 = 1
for i := 0; i < b.N; i++ {
a *= 3
}
}
func Benchmark_Int64Mul(b *testing.B) {
var a int64 = 1
for i := 0; i < b.N; i++ {
a *= 3
}
}
func Benchmark_Float32Mul(b *testing.B) {
var a float32 = 1.0
for i := 0; i < b.N; i++ {
a *= 1.5
}
}
func Benchmark_Float64Mul(b *testing.B) {
var a float64 = 1.0
for i := 0; i < b.N; i++ {
a *= 1.5
}
}
func Benchmark_IntDiv(b *testing.B) {
var a = 0x7FFFFFFFFF
for i := 0; i < b.N; i++ {
a /= 3
}
}
func Benchmark_Int8Div(b *testing.B) {
var a int8 = 0x7F
for i := 0; i < b.N; i++ {
a /= 3
}
}
func Benchmark_Int16Div(b *testing.B) {
var a int16 = 0x7FFF
for i := 0; i < b.N; i++ {
a /= 3
}
}
func Benchmark_Int32Div(b *testing.B) {
var a int32 = 0x7FFFFFFF
for i := 0; i < b.N; i++ {
a /= 3
}
}
func Benchmark_Int64Div(b *testing.B) {
var a int64 = 0x7FFFFFFFFF
for i := 0; i < b.N; i++ {
a /= 3
}
}
func Benchmark_Float32Div(b *testing.B) {
var a = float32(0x7FFFFFFF)
for i := 0; i < b.N; i++ {
a /= 1.5
}
}
func Benchmark_Float64Div(b *testing.B) {
var a = float64(0x7FFFFFFFFF)
for i := 0; i < b.N; i++ {
a /= 1.5
}
}
实验结果：
dada-imac:labs dada$ go test -test.bench="." labs05 testing: warning: no tests to run PASS Benchmark_IntAdd 2000000000 0.28 ns/op Benchmark_Int8Add 2000000000 0.28 ns/op Benchmark_Int16Add 2000000000 0.28 ns/op Benchmark_Int32Add 2000000000 0.28 ns/op Benchmark_Int64Add 2000000000 0.28 ns/op Benchmark_Float32Add 2000000000 1.05 ns/op Benchmark_Float64Add 2000000000 0.79 ns/op Benchmark_IntSub 2000000000 0.28 ns/op Benchmark_Int8Sub 2000000000 0.28 ns/op Benchmark_Int16Sub 2000000000 0.27 ns/op Benchmark_Int32Sub 2000000000 0.28 ns/op Benchmark_Int64Sub 2000000000 0.28 ns/op Benchmark_Float32Sub 2000000000 1.31 ns/op Benchmark_Float64Sub 2000000000 0.87 ns/op Benchmark_IntMul 2000000000 0.79 ns/op Benchmark_Int8Mul 2000000000 0.79 ns/op Benchmark_Int16Mul 2000000000 0.79 ns/op Benchmark_Int32Mul 2000000000 0.79 ns/op Benchmark_Int64Mul 2000000000 0.79 ns/op Benchmark_Float32Mul 2000000000 1.57 ns/op Benchmark_Float64Mul 2000000000 1.31 ns/op Benchmark_IntDiv 2000000000 1.57 ns/op Benchmark_Int8Div 2000000000 1.84 ns/op Benchmark_Int16Div 1000000000 2.08 ns/op Benchmark_Int32Div 2000000000 1.62 ns/op Benchmark_Int64Div 2000000000 1.57 ns/op Benchmark_Float32Div 50000000 44.3 ns/op Benchmark_Float64Div 50000000 48.0 ns/op ok labs05 49.453s
结论：浮点数除法明显更慢，尽量把除法转换为乘法运算。
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